论文标题
Omicron变体的CoVID-19动力学的分数SEIR模型和数据驱动的预测
Fractional SEIR Model and Data-Driven Predictions of COVID-19 Dynamics of Omicron Variant
论文作者
论文摘要
我们研究了Omicron变体通过分数易感性侵蚀的回形剂（SEIR）模型的COVID-19的动态演化。初步数据表明，Omicron感染的症状并不突出，因此传播更加隐藏，这会导致大流行开始时发现的新感染病例相对较慢。为了表征特定的动力学，采用Caputo-Hadamard分数衍生物来完善经典模型。基于报告的数据，我们通过分数物理学的神经网络（FPINNS）推断了分数阶，时间依赖性参数以及分数SEIR模型的未观察到的动力学。然后，我们使用学习的分数SEIR模型进行短时预测。
We study the dynamic evolution of COVID-19 cased by the Omicron variant via a fractional susceptible-exposedinfected-removed (SEIR) model. Preliminary data suggest that the symptoms of Omicron infection are not prominent and the transmission is therefore more concealed, which causes a relatively slow increase in the detected cases of the new infected at the beginning of the pandemic. To characterize the specific dynamics, the Caputo-Hadamard fractional derivative is adopted to refined the classical SEIR model. Based on the reported data, we infer the fractional order, timedependent parameters, as well as unobserved dynamics of the fractional SEIR model via fractional physics-informed neural networks (fPINNs). Then, we make short-time predictions using the learned fractional SEIR model.
